First Observation of an Acetate Switch in a Methanogenic Autotroph (Methanococcus maripaludis S2).

The transition from acetate production by a microorganism in its early growth phase to acetate re-uptake in its late growth phase has been termed acetate switch. It has been observed in several heterotrophic prokaryotes, but not in an autotroph. Furthermore, all reports hitherto have involved the tricarboxylic acid cycle. This study reports the first observation of acetate switch in a methanogenic autotroph Methanococcus maripaludis S2, which uses the Wolfe cycle for its anaerobic respiration. When grown in minimal medium with carbon dioxide as the sole carbon source, and either ammonium or dinitrogen as the sole nitrogen source, M. maripaludis S2 dissimilated acetate in the early growth phase and assimilated it back in the late growth phase. The acetate switch was more pronounced in the dinitrogen-grown cultures. We postulate that the acetate dissimilation in M. maripaludis S2 may serve as a metabolic outlet for the carbon overflow in the early growth phase, and the assimilation in the late growth phase may be due to the scarcity of the carbon source. Based on the primary and secondary protein structures, we propose that MMP0253 may function as the adenosine diphosphate (ADP)-forming acetyl-CoA synthetase to catalyse acetate formation from acetyl-CoA. To verify this, we produced MMP0253 via the ligation-independent cloning technique in Escherichia coli strain Rosetta (DE3) using pNIC28-Bsa4 as the vector. The recombinant protein showed catalytic activity, when added into a mixture of acetyl-CoA, ADP, and inorganic phosphate (Pi). The concentration profile of acetate, together with the enzymatic activity of MMP0253, shows that M. maripaludis S2 can produce acetate and exhibit an acetate switch.

Metabolic processes of Methanococcus maripaludis and potential applications.

Methanococcus maripaludis is a rapidly growing, fully sequenced, genetically tractable model organism among hydrogenotrophic methanogens. It has the ability to convert CO2 and H2 into a useful cleaner energy fuel (CH4). In fact, this conversion enhances in the presence of free nitrogen as the sole nitrogen source due to prolonged cell growth. Given the global importance of GHG emissions and climate change, diazotrophy can be attractive for carbon capture and utilization applications from appropriately treated flue gases, where surplus hydrogen is available from renewable electricity sources. In addition, M. maripaludis can be engineered to produce other useful products such as terpenoids, hydrogen, methanol, etc. M. maripaludis with its unique abilities has the potential to be a workhorse like Escherichia coli and S. cerevisiae for fundamental and experimental biotechnology studies. More than 100 experimental studies have explored different specific aspects of the biochemistry and genetics of CO2 and N2 fixation by M. maripaludis. Its genome-scale metabolic model (iMM518) also exists to study genetic perturbations and complex biological interactions. However, a comprehensive review describing its cell structure, metabolic processes, and methanogenesis is still lacking in the literature. This review fills this crucial gap. Specifically, it integrates distributed information from the literature to provide a complete and detailed view for metabolic processes such as acetyl-CoA synthesis, pyruvate synthesis, glycolysis/gluconeogenesis, reductive tricarboxylic acid (RTCA) cycle, non-oxidative pentose phosphate pathway (NOPPP), nitrogen metabolism, amino acid metabolism, and nucleotide biosynthesis. It discusses energy production via methanogenesis and its relation to metabolism. Furthermore, it reviews taxonomy, cell structure, culture/storage conditions, molecular biology tools, genome-scale models, and potential industrial and environmental applications. Through the discussion, it develops new insights and hypotheses from experimental and modeling observations, and identifies opportunities for further research and applications.

Flux measurements and maintenance energy for carbon dioxide utilization by Methanococcus maripaludis.

BACKGROUND: The rapidly growing mesophilic methanogen Methanococcus maripaludis S2 has a unique ability to consume both CO2 and N2, the main components of a flue gas, and produce methane with H2 as the electron donor. The existing literature lacks experimental measurements of CO2 and H2 uptake rates and CH4 production rates on M. maripaludis. Furthermore, it lacks estimates of maintenance energies for use with genome-scale models. In this paper, we performed batch culture experiments on M. maripaludis S2 using CO2 as the sole carbon substrate to quantify three key extracellular fluxes (CO2, H2, and CH4) along with specific growth rates. For precise computation of these fluxes from experimental measurements, we developed a systematic process simulation approach. Then, using an existing genome-scale model, we proposed an optimization procedure to estimate maintenance energy parameters: growth associated maintenance (GAM) and non-growth associated maintenance (NGAM). RESULTS: The measured extracellular fluxes for M. maripaludis showed excellent agreement with in silico predictions from a validated genome-scale model (iMM518) for NGAM = 7.836 mmol/gDCW/h and GAM = 27.14 mmol/gDCW. M. maripaludis achieved a CO2 to CH4 conversion yield of 70-95 % and a growth yield of 3.549 ± 0.149 g DCW/mol CH4 during the exponential phase. The ATP gain of 0.35 molATP/molCH4 for M. maripaludis, computed using NGAM, is in the acceptable range of 0.3-0.7 mol ATP/molCH4 reported for methanogens. Interestingly, the uptake distribution of amino acids, quantified using iMM518, confirmed alanine to be the most preferred amino acids for growth and methanogenesis. CONCLUSIONS: This is the first study to report experimental gas consumption and production rates for the growth of M. maripaludis on CO2 and H2 in minimal media. A systematic process simulation and optimization procedure was successfully developed to precisely quantify extracellular fluxes along with cell growth and maintenance energy parameters. Our growth yields, ATP gain, and energy parameters fall within acceptable ranges known in the literature for hydrogenotrophic methanogens.

Genome-scale metabolic network reconstruction and in silico flux analysis of the thermophilic bacterium Thermus thermophilus HB27.

BACKGROUND: Thermus thermophilus, an extremely thermophilic bacterium, has been widely recognized as a model organism for studying how microbes can survive and adapt under high temperature environment. However, the thermotolerant mechanisms and cellular metabolism still remains mostly unravelled. Thus, it is highly required to consider systems biological approaches where T. thermophilus metabolic network model can be employed together with high throughput experimental data for elucidating its physiological characteristics under such harsh conditions. RESULTS: We reconstructed a genome-scale metabolic model of T. thermophilus, iTT548, the first ever large-scale network of a thermophilic bacterium, accounting for 548 unique genes, 796 reactions and 635 unique metabolites. Our initial comparative analysis of the model with Escherichia coli has revealed several distinctive metabolic reactions, mainly in amino acid metabolism and carotenoid biosynthesis, producing relevant compounds to retain the cellular membrane for withstanding high temperature. Constraints-based flux analysis was, then, applied to simulate the metabolic state in glucose minimal and amino acid rich media. Remarkably, resulting growth predictions were highly consistent with the experimental observations. The subsequent comparative flux analysis under different environmental conditions highlighted that the cells consumed branched chain amino acids preferably and utilized them directly in the relevant anabolic pathways for the fatty acid synthesis. Finally, gene essentiality study was also conducted via single gene deletion analysis, to identify the conditional essential genes in glucose minimal and complex media. CONCLUSIONS: The reconstructed genome-scale metabolic model elucidates the phenotypes of T. thermophilus, thus allowing us to gain valuable insights into its cellular metabolism through in silico simulations. The information obtained from such analysis would not only shed light on the understanding of physiology of thermophiles but also helps us to devise metabolic engineering strategies to develop T. thermophilus as a thermostable microbial cell factory.

NADPH-dependent pgi-gene knockout Escherichia coli metabolism producing shikimate on different carbon sources.

We explored the physiological and metabolic effects of different carbon sources (glucose, fructose, and glucose/fructose mixture) in phosphoglucose isomerase (pgi) knockout Escherichia coli mutant producing shikimic acid (SA). It was observed that the pgi(-) mutant grown on glucose exhibited significantly lower cell growth compared with the pgi(+) strain and its mixed glucose/fructose fermentation grew well. Interestingly, when fructose was used as a carbon source, the pgi(-) mutant showed the enhanced SA production compared with the pgi(+) strain. In silico analysis of a genome-scale E. coli model was then conducted to characterize the cellular metabolism and quantify NAPDH regeneration, which allowed us to understand such experimentally observed attenuated cell growth and enhanced SA production in glucose- and fructose-consuming pgi(-) mutant, respectively with respect to cofactor regeneration.

Genome-scale modeling and in silico analysis of ethanologenic bacteria Zymomonas mobilis.

Bioethanol has been recognized as a potential alternative energy source. Among various ethanol-producing microbes, Zymomonas mobilis has acquired special attention due to its higher ethanol yield and tolerance. However, cellular metabolism in Z. mobilis remains unclear, hindering its practical application for bioethanol production. To elucidate such physiological characteristics, we reconstructed and validated a genome-scale metabolic network (iZM363) of Z. mobilis ATCC31821 (ZM4) based on its annotated genome and biochemical information. The phenotypic behaviors and metabolic states predicted by our genome-scale model were highly consistent with the experimental observations of Z. mobilis ZM4 strain growing on glucose as well as NMR-measured intracellular fluxes of an engineered strain utilizing glucose, fructose, and xylose. Subsequent comparative analysis with Escherichia coli and Saccharomyces cerevisiae as well as gene essentiality and flux coupling analyses have also confirmed the functional role of pdc and adh genes in the ethanologenic activity of Z. mobilis, thus leading to better understanding of this natural ethanol producer. In future, the current model could be employed to identify potential cell engineering targets, thereby enhancing the productivity of ethanol in Z. mobilis.

Flux-based analysis of sulfur metabolism in desulfurizing strains of Rhodococcus erythropolis.

Rhodococcus erythropolis has been studied widely for potential applications in biodesulfurization. Previous works have been largely experimental with an emphasis on the characterization and genetic engineering of desulfurizing strains for improved biocatalysis. A systems modeling approach that can complement these experimental efforts by providing useful insights into the complex interactions of desulfurization reactions with various other metabolic activities is absent in the literature. In this work, we report the first attempt at reconstructing a flux-based model to analyze sulfur utilization by R. erythropolis. The model includes the 4S pathway for dibenzothiophene (DBT) desulfurization. It predicts closely the growth rates reported by two independent experimental studies, and gives a clear and comprehensive picture of the pathways that assimilate the sulfur from DBT into biomass. In addition, it successfully elucidates that sulfate promotes higher cell growth than DBT and its presence in the medium reduces DBT desulfurization rates. A study using eight carbon sources suggests that ethanol and lactate yield higher cell growth and desulfurization rates than citrate, fructose, glucose, gluconate, glutamate, and glycerol.

Combined data preprocessing and multivariate statistical analysis characterizes fed-batch culture of mouse hybridoma cells for rational medium design.

We present an integrated framework for characterizing fed-batch cultures of mouse hybridoma cells producing monoclonal antibody (mAb). This framework systematically combines data preprocessing, elemental balancing and statistical analysis technique. Initially, specific rates of cell growth, glucose/amino acid consumptions and mAb/metabolite productions were calculated via curve fitting using logistic equations, with subsequent elemental balancing of the preprocessed data indicating the presence of experimental measurement errors. Multivariate statistical analysis was then employed to understand physiological characteristics of the cellular system. The results from principal component analysis (PCA) revealed three major clusters of amino acids with similar trends in their consumption profiles: (i) arginine, threonine and serine, (ii) glycine, tyrosine, phenylalanine, methionine, histidine and asparagine, and (iii) lysine, valine and isoleucine. Further analysis using partial least square (PLS) regression identified key amino acids which were positively or negatively correlated with the cell growth, mAb production and the generation of lactate and ammonia. Based on these results, the optimal concentrations of key amino acids in the feed medium can be inferred, potentially leading to an increase in cell viability and productivity, as well as a decrease in toxic waste production. The study demonstrated how the current methodological framework using multivariate statistical analysis techniques can serve as a potential tool for deriving rational medium design strategies.

Genome-scale modeling and in silico analysis of mouse cell metabolic network.

Genome-scale metabolic modeling has been successfully applied to a multitude of microbial systems, thus improving our understanding of their cellular metabolisms. Nevertheless, only a handful of works have been done for describing mammalian cells, particularly mouse, which is one of the important model organisms, providing various opportunities for both biomedical research and biotechnological applications. Presented herein is a genome-scale mouse metabolic model that was systematically reconstructed by improving and expanding the previous generic model based on integrated biochemical and genomic data of Mus musculus. The key features of the updated model include additional information on gene-protein-reaction association, and improved network connectivity through lipid, amino acid, carbohydrate and nucleotide biosynthetic pathways. After examining the model predictability both quantitatively and qualitatively using constraints-based flux analysis, the structural and functional characteristics of the mouse metabolism were investigated by evaluating network statistics/centrality, gene/metabolite essentiality and their correlation. The results revealed that overall mouse metabolic network is topologically dominated by highly connected and bridging metabolites, and functionally by lipid metabolism that most of essential genes and metabolites are from. The current in silico mouse model can be exploited for understanding and characterizing the cellular physiology, identifying potential cell engineering targets for the enhanced production of recombinant proteins and developing diseased state models for drug targeting.

Strain improvement and process development for biobutanol production.

Among various renewable and sustainable energy sources being explored, biobutanol has been recognized as one of the promising alternatives for biofuels due to its attractive physical and chemical properties. Various inventions have been reported for the biological production of butanol, maintaining its competitiveness in efficiency, economy, and production scale. This article reviews such patented inventions and relevant research achievements to the microbial strains and fermentation processes for the enhanced production of biobutanol. We also discuss current challenges for strain improvement based on recent developments.

Characterizing Escherichia coli DH5alpha growth and metabolism in a complex medium using genome-scale flux analysis.

Genome-scale flux analysis of Escherichia coli DH5alpha growth in a complex medium was performed to investigate the relationship between the uptake of various nutrients and their metabolic outcomes. During the exponential growth phase, we observed a sequential consumption order of serine, aspartate and glutamate in the complex medium as well as the complete consumption of key carbohydrate nutrients, glucose and trehalose. Based on the consumption and production rates of the measured metabolites, constraints-based flux analysis of a genome-scale E. coli model was then conducted to elucidate their utilization in the metabolism. The in silico analysis revealed that the cell exploited biosynthetic precursors taken up directly from the complex medium, through growth-related anabolic pathways. This suggests that the cell could be functioning in an energetically more efficient manner by reducing the energy needed to produce amino acids. The in silico simulation also allowed us to explain the observed rapid consumption of serine: excessively consumed external serine from the complex medium was mainly converted into pyruvate and glycine, which in turn, led to the acetate accumulation. The present work demonstrates the application of an in silico modeling approach to characterizing microbial metabolism under complex medium condition. This work further illustrates the use of in silico genome-scale analysis for developing better strategies related to improving microbial growth and enhancing the productivity of desirable metabolites.

Web-based applications for building, managing and analysing kinetic models of biological systems.

Mathematical modelling and computational analysis play an essential role in improving our capability to elucidate the functions and characteristics of complex biological systems such as metabolic, regulatory and cell signalling pathways. The modelling and concomitant simulation render it possible to predict the cellular behaviour of systems under various genetically and/or environmentally perturbed conditions. This motivates systems biologists/bioengineers/bioinformaticians to develop new tools and applications, allowing non-experts to easily conduct such modelling and analysis. However, among a multitude of systems biology tools developed to date, only a handful of projects have adopted a web-based approach to kinetic modelling. In this report, we evaluate the capabilities and characteristics of current web-based tools in systems biology and identify desirable features, limitations and bottlenecks for further improvements in terms of usability and functionality. A short discussion on software architecture issues involved in web-based applications and the approaches taken by existing tools is included for those interested in developing their own simulation applications.

Elucidation of metabolism in hybridoma cells grown in fed-batch culture by genome-scale modeling.

Genome-scale modeling of mouse hybridoma cells producing monoclonal antibodies (mAb) was performed to elucidate their physiological and metabolic states during fed-batch cell culture. Initially, feed media nutrients were monitored to identify key components among carbon sources and amino acids with significant impact on the desired outcome, for example, cell growth and antibody production. The monitored profiles indicated rapid assimilation of glucose and glutamine during the exponential growth phase. Significant increase in mAb concentration was also observed when glutamine concentration was controlled at 0.5 mM as a feeding strategy. Based on the reconstructed genome-scale metabolic network of mouse hybridoma cells and fed-batch profiles, flux analysis was then implemented to investigate the cellular behavior and changes in internal fluxes during the cell culture. The simulated profile of the cell growth was consistent with experimentally measured specific growth rate. The in silico simulation results indicated (i) predominant utilization of glycolytic pathway for ATP production, (ii) importance of pyruvate node in metabolic shifting, and (iii) characteristic pattern in lactate to glucose ratio during the exponential phase. In future, experimental and in silico analyses can serve as a promising approach to identifying optimal feeding strategies and potential cell engineering targets as well as facilitate media optimization for the enhanced production of mAb or recombinant proteins in mammalian cells.